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∙ 09/19/2023

Test-Time Training for Speech

In this paper, we study the application of Test-Time Training (TTT) as a...

0 Sri Harsha Dumpala, et al. ∙

∙ 06/15/2023

Training Diffusion Classifiers with Denoising Assistance

Score-matching and diffusion models have emerged as state-of-the-art gen...

0 Chandramouli Sastry, et al. ∙

∙ 04/18/2023

Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models

Text-to-image generative models have demonstrated remarkable capabilitie...

0 Stephen Brade, et al. ∙

∙ 07/26/2022

Generative Extraction of Audio Classifiers for Speaker Identification

It is perhaps no longer surprising that machine learning models, especia...

0 Tejumade Afonja, et al. ∙

∙ 02/19/2022

Echofilter: A Deep Learning Segmentation Model Improves the Automation, Standardization, and Timeliness for Post-Processing Echosounder Data in Tidal Energy Streams

Understanding the abundance and distribution of fish in tidal energy str...

0 Scott C. Lowe, et al. ∙

∙ 11/02/2021

LogAvgExp Provides a Principled and Performant Global Pooling Operator

We seek to improve the pooling operation in neural networks, by applying...

0 Scott C. Lowe, et al. ∙

∙ 10/22/2021

Logical Activation Functions: Logit-space equivalents of Boolean Operators

Neuronal representations within artificial neural networks are commonly ...

5 Scott C. Lowe, et al. ∙

∙ 08/02/2021

Musical Speech: A Transformer-based Composition Tool

In this paper, we propose a new compositional tool that will generate a ...

0 Jason d'Eon, et al. ∙

∙ 07/24/2021

Significance of Speaker Embeddings and Temporal Context for Depression Detection

Depression detection from speech has attracted a lot of attention in rec...

0 Sri Harsha Dumpala, et al. ∙

∙ 12/28/2019

Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices

When presented with Out-of-Distribution (OOD) examples, deep neural netw...

0 Chandramouli Shama Sastry, et al. ∙

∙ 07/09/2019

Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls

Performance RNN is a machine-learning system designed primarily for the ...

0 Nicholas Meade, et al. ∙

∙ 11/22/2018

TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer

In this work, we address the problem of musical timbre transfer, where t...

0 Sicong Huang, et al. ∙

∙ 08/10/2018

This Time with Feeling: Learning Expressive Musical Performance

Music generation has generally been focused on either creating scores or...

0 Sageev Oore, et al. ∙

∙ 10/30/2017

Onsets and Frames: Dual-Objective Piano Transcription

We consider the problem of transcribing polyphonic piano music with an e...

0 Curtis Hawthorne, et al. ∙

∙ 06/14/2017

Learning and Evaluating Musical Features with Deep Autoencoders

In this work we describe and evaluate methods to learn musical embedding...

0 Mason Bretan, et al. ∙

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